Intelligent Classifiers for Football Player Performance Based on Machine Learning Models

The remarkable effectiveness of Machine Learning (ML) methodologies has led to a significant increase in their application across various academic domains, particularly in diverse sports sectors. Over the past decade, scholars have utilized Machine Learning (ML) algorithms in football for varied objectives, encompassing the analysis of football players' performances, injury prediction, market value forecasting, and action recognition. Nevertheless, there has been a scarcity of research addressing the evaluation of football players' performance, which is a noteworthy concern for coaches. Hence, the objective of this work is to categorize the performance of football players into active, normal, or weak based on activity features. This will be achieved through the utilization of the Performance Evaluation Machine Learning Model (PEMLM), employing two novel datasets that cover both training and match sessions. To attain this goal, seven machine learning methods are applied, namely Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, and K-Nearest Neighbor. The findings indicate that in the dataset corresponding to match sessions, the Decision Tree classifier attains the highest accuracy (100%) and the shortest test time. In contrast, the K-Nearest Neighbor demonstrates the best accuracy (96%) and a reasonable test time for the training dataset. These reported metrics underscore the reliability and validity of the proposed assessment approach in evaluating the performance of football players in online games. The results are verified and the models are assessed for overfitting through a k-fold cross-validation process.